Robots and Sensors for Neurologic Rehabilitation: What Have We Learned and What Comes Next?
Speaker: Prof. David Reinkensmeyer
Irvine, University of California
October 1st, 2024 | 3.00 pm
DEIB - Carlo Erba Room (Bld. 7)
Piazza Leonardo da Vinci, 32
On line by Webex
Contact: Prof. Monica Soncini
Irvine, University of California
October 1st, 2024 | 3.00 pm
DEIB - Carlo Erba Room (Bld. 7)
Piazza Leonardo da Vinci, 32
On line by Webex
Contact: Prof. Monica Soncini
Abstract
On October 1st, 2024 at 3.00 pm the seminar titeld "Robots and Sensors for Neurologic Rehabilitation: What Have We Learned and What Comes Next?" will take place at DEIB Carlo Erba Room (Building 7) and on line by Webex.
Starting about 40 years ago, researchers began developing robotic and sensor-based technologies to help deliver and quantify movement practice after neurologic injuries.
In this talk I will discuss how technology has given us new insights into hand function after stroke. Then I will provide an overview of key findings from clinical and home-based testing of rehabilitation robots and sensors. Based on this, I will suggest several needed directions for future research.
David Reinkensmeyer is Professor in the Departments of Mechanical and Aerospace Engineering, Anatomy and Neurobiology, Biomedical Engineering, and Physical Medicine and Rehabilitation at the University of California at Irvine. He received the B.S. degree in electrical engineering from the Massachusetts Institute of Technology and the M.S. and Ph.D. degrees in electrical engineering from the University of California at Berkeley, studying robotics and the neuroscience of human movement. He carried out postdoctoral studies at the Rehabilitation Institute of Chicago developing robotic devices for rehabilitation therapy after stroke before becoming a assistant professor at U.C. Irvine in 1998. He is co-inventor of the T-WREX upper extremity training device, which was commercialized as ArmeoSpring, and the MusicGlove finger training device. He is Editor-in-Chief of the Journal of Neuroengineering and Rehabilitation, co-director of the NIDILRR COMET Robotic Rehabilitation Engineering Center, co-editor of the of the book Neurorehabilitation Technology, 3rd edition, and a fellow of the National Academy of Inventors.
Scientific area: robotics, rehabilitation, neuroscience, motor control, motor learning, sensors, computational models.
Starting about 40 years ago, researchers began developing robotic and sensor-based technologies to help deliver and quantify movement practice after neurologic injuries.
In this talk I will discuss how technology has given us new insights into hand function after stroke. Then I will provide an overview of key findings from clinical and home-based testing of rehabilitation robots and sensors. Based on this, I will suggest several needed directions for future research.
David Reinkensmeyer is Professor in the Departments of Mechanical and Aerospace Engineering, Anatomy and Neurobiology, Biomedical Engineering, and Physical Medicine and Rehabilitation at the University of California at Irvine. He received the B.S. degree in electrical engineering from the Massachusetts Institute of Technology and the M.S. and Ph.D. degrees in electrical engineering from the University of California at Berkeley, studying robotics and the neuroscience of human movement. He carried out postdoctoral studies at the Rehabilitation Institute of Chicago developing robotic devices for rehabilitation therapy after stroke before becoming a assistant professor at U.C. Irvine in 1998. He is co-inventor of the T-WREX upper extremity training device, which was commercialized as ArmeoSpring, and the MusicGlove finger training device. He is Editor-in-Chief of the Journal of Neuroengineering and Rehabilitation, co-director of the NIDILRR COMET Robotic Rehabilitation Engineering Center, co-editor of the of the book Neurorehabilitation Technology, 3rd edition, and a fellow of the National Academy of Inventors.
Scientific area: robotics, rehabilitation, neuroscience, motor control, motor learning, sensors, computational models.